Understanding work behaviors in remote work environments during the COVID-19 pandemic: Transaction cost theory perspective

Previous studies on remote work have not fully understood which roles are suitable for remote work. In our study, we performed the literature review method and developed a conceptual model inspired by transaction cost theory. Additionally, we believe remote work is an optional option in the context of hybrid work during COVID-19. Our conceptual model leads us to believe that remote workers incur some additional perceived costs in the remote work process. We analyze the following four different roles to understand their perceived costs of working remotely: CEO, product manager, database engineer, and administrative employee. We are expected to provide theoretical explanations for what factors influence remote workers\u27 perceived transaction costs

Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations

Large language models (LMs) have exhibited superior in-context learning (ICL) ability to adopt to target tasks by prompting with a few input-output demonstrations. Towards better ICL, different methods are proposed to select representative demonstrations from existing training corpora. However, such a setting is not aligned with real-world practices, as end-users usually query LMs without accesses to demonstration pools. Inspired by evidence suggesting LMs' zero-shot capabilities are underrated, and the role of demonstrations are primarily for exposing models' intrinsic functionalities, we introduce Self-ICL, a simple framework for zero-shot ICL. Given a test input, Self-ICL first prompts the model to generate pseudo-inputs. Next, the model predicts pseudo-labels for the pseudo-inputs via zero-shot prompting. Finally, we construct pseudo-demonstrations from pseudo-input-label pairs, and perform ICL for the test input. Evaluation on BIG-Bench Hard shows Self-ICL steadily surpasses zero-shot and zero-shot chain-of-thought baselines on head-to-head and all-task average performance. Our findings suggest the possibility to bootstrap LMs' intrinsic capabilities towards better zero-shot performance.Comment: Work in progres

Large Language Models Perform Diagnostic Reasoning

We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT). Empirical results demonstrate that by simply prompting large language models trained only on general text corpus with two DR-CoT exemplars, the diagnostic accuracy improves by 15% comparing to standard prompting. Moreover, the gap reaches a pronounced 18% in out-domain settings. Our findings suggest expert-knowledge reasoning in large language models can be elicited through proper promptings.Comment: Accepted as a Tiny Paper at ICLR 2023 (10 pages, 5 figures

Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation

In this paper, we address the hallucination problem commonly found in natural language generation tasks. Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies. We propose a new decoding method called Fidelity-Enriched Contrastive Search (FECS), which augments the contrastive search framework with context-aware regularization terms. FECS promotes tokens that are semantically similar to the provided source while penalizing repetitiveness in the generated text. We demonstrate its effectiveness across two tasks prone to hallucination: abstractive summarization and dialogue generation. Results show that FECS consistently enhances faithfulness across various language model sizes while maintaining output diversity comparable to well-performing decoding algorithms.Comment: Accepted as a short paper at EMNLP 202

Impacts of Techno-Dependence in The Mobile Instant Messaging Environment

Mobile Instant Messaging (MIM) services such as LINE, WhatsApp, Facebook Messenger and WeChat have already established a mobile communication environment that extends beyond words and sounds. As MIM provides an effective way to communicate, it can improve workplace efficiency, whether within an organization or among offices spread around the world. Nowadays, MIM has been widely accepted as both a social and work tool. The social interaction overload generated by SNSs contributes to emotional exhaustion. Emotional exhaustion, on the other hand, leads to dissatisfaction and discontinuous usage intentions. In the context of MIMs (since they are relative new apps), users whether are likely to experience emotional exhaustion that is generated by social interaction overload, and therefore discontinue their use of MIMs. In contrast to traditional research on IS continued use in the past which defines dependence as a routine and unconscious usage pattern. MIMs offer a communication method that is faster and easier than phone calls or SMS. It is possible that MIMs bring people closer by allowing their users to understand more of the situational matters related to their friends or family, without being limited by distance. Specifically, social-group functions offered by LINE can encourage users to join certain social groups (for instance, family, colleagues, classmates, or friends). Group members can not only discuss common topics, they can also share their “photo albums,” enabling members to enhance their sense of belonging. At the same time, they have the opportunity to feel a sense of being valued, loved, and needed. Although the mobility and accessibility of mobile devices allow users to instantly contact each other on MIMs and on real-time basis, excessive use of MIMs, or MIM techno-dependence, is likely to generate social-related stress among their users. Therefore, this research attempts to explore the possibility that MIM techno-dependency can have non-detrimental effects, and considers the positive and healthy results from MIM techno-dependency due to an increased sense of belonging. The questions explored include: do MIMs users develop a positive techno-dependence? Does this positive emotional reaction encourage MIMs users to continue their use of MIMs? Since LINE is a relative newcomer to MIM, there is still a dearth of research needed to explore issues related to using MIM as a research tool. This study considers how LINE combines a diverse range of communication approaches—such as voice, texts, maps, pictures, photos, locations, video, and audio—with a variety of community groups such as friends chat, group chat, dynamic news, and official accounts. It seems worthwhile to study characteristics of LINE’s users in order to further explore the issues related to MIM. Through the hypotheses development and a survey research on 685 LINE users, this study inferred that users make frequent use of LINE in the long-term mainly because of four kinds of techno-dependence: people, fun, information, and work. Such techno-dependence generates positive and negative consequences concurrently. On the one hand, because the user’s dependence on LINE enhances his or her belongingness through friends, colleagues, and family, this positive social and emotional reaction will make users satisfied with LINE, and thus increase continuous usage intention for LINE. On the other hand, the user’s dependence on LINE means that they experience social interaction overload resulting in emotional exhaustion. Dependence on LINE leads to users experiencing pressure from both social message overload and social demand overload, resulting in social interaction overload. This negative social and emotional reaction will cause a decrease in user satisfaction with LINE, thereby reducing the continuous usage intention of LINE. Based on these findings, we suggest that LINE-related techno-dependence can enable users to increase their sense of positive social belongingness, but can also cause negative social interaction overload. It is concluded that the consequences of techno-dependence are characterized by both positive and negative emotions. Users’ evaluations of LINE are simultaneously affected by positive and negative social and emotional factors

Efficient Maxima-Finding Algorithms for Random Planar Samples

this paper a simple classification of several known algorithms for finding the maxima, together with several new algorithms; among these are two efficient algorithms---one with expected complexity n +O( # nlogn) when the point samples are issued from some planar regions, and another more efficient than existing one